Tapani Raiko

According to our database1, Tapani Raiko authored at least 66 papers between 2002 and 2016.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2016
Semi-Supervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentation.
CoRR, 2016

How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks.
CoRR, 2016

A Character-Word Compositional Neural Language Model for Finnish.
CoRR, 2016

Ladder Variational Autoencoders.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

DopeLearning: A Computational Approach to Rap Lyrics Generation.
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Semi-supervised Domain Adaptation for Weakly Labeled Semantic Video Object Segmentation.
Proceedings of the Computer Vision - ACCV 2016, 2016

2015
Two-layer contractive encodings for learning stable nonlinear features.
Neural Networks, 2015

Measuring the usefulness of hidden units in Boltzmann machines with mutual information.
Neural Networks, 2015

Self-organization and missing values in SOM and GTM.
Neurocomputing, 2015

Lateral Connections in Denoising Autoencoders Support Supervised Learning.
CoRR, 2015

Semi-Supervised Learning with Ladder Network.
CoRR, 2015

DopeLearning: A Computational Approach to Rap Lyrics Generation.
CoRR, 2015

Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters.
CoRR, 2015

Bidirectional Recurrent Neural Networks as Generative Models - Reconstructing Gaps in Time Series.
CoRR, 2015

Semi-supervised Learning with Ladder Networks.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Bidirectional Recurrent Neural Networks as Generative Models.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014
Denoising autoencoder with modulated lateral connections learns invariant representations of natural images.
CoRR, 2014

Iterative Neural Autoregressive Distribution Estimator (NADE-k).
CoRR, 2014

Techniques for Learning Binary Stochastic Feedforward Neural Networks.
CoRR, 2014

Linear State-Space Model with Time-Varying Dynamics.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

Iterative Neural Autoregressive Distribution Estimator NADE-k.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013
Enhanced Gradient for Training Restricted Boltzmann Machines.
Neural Computation, 2013

Pushing Stochastic Gradient towards Second-Order Methods -- Backpropagation Learning with Transformations in Nonlinearities
CoRR, 2013

Stochastic Gradient Estimate Variance in Contrastive Divergence and Persistent Contrastive Divergence.
CoRR, 2013

Gaussian-Bernoulli deep Boltzmann machine.
Proceedings of the 2013 International Joint Conference on Neural Networks, 2013

Variational Bayesian PCA versus k-NN on a Very Sparse Reddit Voting Dataset.
Proceedings of the Advances in Intelligent Data Analysis XII, 2013

Pushing Stochastic Gradient towards Second-Order Methods - Backpropagation Learning with Transformations in Nonlinearities.
Proceedings of the Neural Information Processing - 20th International Conference, 2013

Two-Layer Contractive Encodings with Shortcuts for Semi-supervised Learning.
Proceedings of the Neural Information Processing - 20th International Conference, 2013

Measuring the Usefulness of Hidden Units in Boltzmann Machines with Mutual Information.
Proceedings of the Neural Information Processing - 20th International Conference, 2013

Gaussian-Bernoulli restricted Boltzmann machines and automatic feature extraction for noise robust missing data mask estimation.
Proceedings of the IEEE International Conference on Acoustics, 2013

A Two-Stage Pretraining Algorithm for Deep Boltzmann Machines.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2013, 2013

2012
Deep Learning Made Easier by Linear Transformations in Perceptrons.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Machine learning for signal processing 2010.
Neurocomputing, 2012

Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework
CoRR, 2012

'Say EM' for Selecting Probabilistic Models for Logical Sequences
CoRR, 2012

Controlling Self-Organization and Handling Missing Values in SOM and GTM.
Proceedings of the Advances in Self-Organizing Maps - 9th International Workshop, 2012

Semi-supervised detection of collective anomalies with an application in high energy particle physics.
Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), 2012

Hybrid Bilinear and Trilinear Models for Exploratory Analysis of Three-Way Poisson Counts.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2012, 2012

Gated Boltzmann Machine in Texture Modeling.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2012, 2012

Tikhonov-Type Regularization for Restricted Boltzmann Machines.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2012, 2012

Learning Deep Belief Networks from Non-stationary Streams.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2012, 2012

Towards Generalizing the Success of Monte-Carlo Tree Search beyond the Game of Go.
Proceedings of the ECAI 2012, 2012

2011
Missing-Feature Reconstruction With a Bounded Nonlinear State-Space Model.
IEEE Signal Process. Lett., 2011

Logical Hidden Markov Models
CoRR, 2011

Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines.
Proceedings of the 28th International Conference on Machine Learning, 2011

Improved Learning of Gaussian-Bernoulli Restricted Boltzmann Machines.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2011, 2011

2010
Practical Approaches to Principal Component Analysis in the Presence of Missing Values.
Journal of Machine Learning Research, 2010

Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes.
Journal of Machine Learning Research, 2010

Document classification utilising ontologies and relations between documents.
Proceedings of the Eighth Workshop on Mining and Learning with Graphs, 2010

Extending Self-Organizing Maps with uncertainty information of probabilistic PCA.
Proceedings of the International Joint Conference on Neural Networks, 2010

Parallel tempering is efficient for learning restricted Boltzmann machines.
Proceedings of the International Joint Conference on Neural Networks, 2010

2009
Variational Bayesian learning of nonlinear hidden state-space models for model predictive control.
Neurocomputing, 2009

A gradient-based algorithm competitive with variational Bayesian EM for mixture of Gaussians.
Proceedings of the International Joint Conference on Neural Networks, 2009

Transformations for variational factor analysis to speed up learning.
Proceedings of the ESANN 2009, 2009

2007
Building Blocks for Variational Bayesian Learning of Latent Variable Models.
Journal of Machine Learning Research, 2007

Principal Component Analysis for Sparse High-Dimensional Data.
Proceedings of the Neural Information Processing, 14th International Conference, 2007

Natural Conjugate Gradient in Variational Inference.
Proceedings of the Neural Information Processing, 14th International Conference, 2007

Principal Component Analysis for Large Scale Problems with Lots of Missing Values.
Proceedings of the Machine Learning: ECML 2007, 2007

2006
Logical Hidden Markov Models.
J. Artif. Intell. Res., 2006

State Inference in Variational Bayesian Nonlinear State-Space Models.
Proceedings of the Independent Component Analysis and Blind Signal Separation, 2006

2005
"Say EM" for Selecting Probabilistic Models for Logical Sequences.
Proceedings of the UAI '05, 2005

Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework.
Proceedings of the UAI '05, 2005

Nonlinear Relational Markov Networks with an Application to the Game of Go.
Proceedings of the Artificial Neural Networks: Formal Models and Their Applications, 2005

2003
Towards Discovering Structural Signatures of Protein Folds Based on Logical Hidden Markov Models.
Proceedings of the 8th Pacific Symposium on Biocomputing, 2003

2002
Logical Hidden Markov Models (Extendes abstract).
Proceedings of the First European Workshop on Probabilistic Graphical Models, 6-8 November - 2002, 2002


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